Title: Security overview and performance assessment of fully homomorphic encryption in machine learning

Authors: Davor Vinko; Kruno Miličević; Ivan Uglik; Adrijan Đurin; Madhurima Ray; Richard Lomotey

Addresses: Random Red Ltd., Ivana Zajca 40, 31000 Osijek, Croatia ' Random Red Ltd., Ivana Zajca 40, 31000 Osijek, Croatia ' Random Red Ltd., Ivana Zajca 40, 31000 Osijek, Croatia ' Random Red Ltd., Ivana Zajca 40, 31000 Osijek, Croatia ' Penn State Beaver, 100 University Drive, Monaca, PA 15061, USA ' Penn State Beaver, 100 University Drive, Monaca, PA 15061, USA

Abstract: In the era of cloud computing and third-party data processing, protecting data during computation remains a major challenge, as traditional encryption secures data only at rest and in transit. Fully homomorphic encryption (FHE) addresses this limitation by enabling computation directly on encrypted data. This paper evaluates the performance of FHE for machine learning using concrete ML, a library designed for FHE-based models. Ten machine learning and deep learning algorithms were implemented using both scikit-learn and concrete ML to compare training time, execution time, and accuracy. While FHE enables execution on encrypted data, only one of the tested algorithms supports training on encrypted data; the remaining models require plaintext training. Results indicate a significant computational overhead for FHE-based models, whereas accuracy remained comparable, with deviations typically below 1% and never exceeding 5%. Despite current performance limitations, FHE offers inherent resistance to quantum attacks and strong potential for privacy-preserving machine learning.

Keywords: security; fully homomorphic encryption; FHE; machine learning; performance assessment; data protection; data encryption; data processing; encryption methods; deep learning.

DOI: 10.1504/IJICS.2026.153793

International Journal of Information and Computer Security, 2026 Vol.30 No.1, pp.1 - 18

Received: 15 May 2025
Accepted: 31 Dec 2025
Published online: 26 May 2026 *

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